I Pointed My Industrial IoT Platform at My Dog β€” Here Is What Happened An engineer at DGIOT, an open-source industrial IoT platform, demonstrated that a dog is just another telemetry source by building PawTrack, a dog health monitoring system, in one afternoon. The system uses the company's production-grade platform, including the DLAS ontology engine, to model breed-specific health risks like hip dysplasia in Golden Retrievers, and shadow devices to handle network disruptions. The demo simulates a smart collar streaming vitals via MQTT, with alerts triggered by ontology-based reasoning. Dog Days Challenge Entry 🐢 My company builds industrial IoT infrastructure β€” 928 gateways monitoring oil wells, UAV testing lines producing 60 drones a day, maritime safety platforms with 99.9999% uptime. This weekend, I pointed that entire stack at a Golden Retriever. Meet PawTrack β€” a dog health monitoring demo built on DGIOT, our open-source industrial IoT platform: Smart Collar PawCollar-V2 β”‚ MQTT Β· 2-second sampling β–Ό DGIOT Platform Erlang/OTP β”‚ 300+ protocol adapters Β· Shadow devices Β· Rule engine β”œβ”€β”€ TDengine time-series storage β”œβ”€β”€ DLAS Ontology Engine OWL reasoning └── Dashboard real-time vitals The collar streams four metrics every 2 seconds: Here's the part that surprised me: a dog is just another telemetry source . The industrial world has spent decades solving problems that pet tech startups are rediscovering: | Industrial Problem | Pet Equivalent | |---|---| | Gateway goes offline in a remote oil field | Dog runs out of Wi-Fi range | | Sensor drift on a pressure gauge | Collar temperature calibration | | False alarms flooding operators | "Your dog is dying" notification at 3am | | 928 gateways to manage | Multiple pets, multiple collars | We already solved all of these for oil fields. A Golden Retriever is just a smaller, fluffier oil well. The interesting part is our DLAS ontology engine . Instead of hard-coded thresholds "if temp 39.5, alert" , we model knowledge : GoldenRetriever βŠ‘ Dog βŠ“ βˆƒhasRisk.HipDysplasia ActivityDrop dog, 40% ∧ BreedRisk dog, High β†’ Alert medium This means the system knows that a Golden Retriever's activity drop is more concerning than a Border Collie's β€” hip dysplasia is breed-specific. The ontology compiles to Erlang pattern matches, so reasoning happens at runtime speed. Every physical device gets a digital twin β€” a gen statem process that mirrors its lifecycle: init β†’ auth β†’ online β†’ {active, inactive, alert} When the collar drops offline, the shadow holds state and retries silently. The dashboard doesn't scream "DOG IS GONE" β€” it waits, because shadows absorb network chaos. This is the same mechanism that keeps oil field gateways calm during sandstorms. The live demo simulates the MQTT stream with real-time vitals, ontology inference, and the alert closed-loop discover β†’ confirm β†’ handle β†’ verify : dgiot/pets/wanCai/telemetry β†’ {"temp":38.6,"hr":88,"activity":62} dgiot/pets/wanCai/shadow β†’ {"state":"active","battery":87,"rssi":-62} Watch for the activity-drop alert β€” when activity falls below 35%, the ontology fires a hip dysplasia risk warning for the Golden Retriever, generates a work order, and closes the loop when activity recovers. Real : DGIOT platform, DLAS ontology engine, shadow device architecture, MQTT pipeline, alert closed-loop β€” all production code from our industrial deployments. Demo : The collar hardware simulated , the dog borrowed , the vitals synthetic values . The point: the platform is production-grade. Building pet monitoring on it took one afternoon, because the hard parts β€” protocol handling, shadow state, ontology reasoning β€” were already done for 300+ industrial use cases. Want to build your own? The platform deploys in 6 minutes: git clone https://github.com/dgiot/dgiot.git cd dgiot && docker-compose up -d Built for the Dev.to Dog Days Challenge. If your dog's activity drops below 35%, take them to the vet β€” the ontology says so.